Overview

Dataset statistics

Number of variables14
Number of observations6497
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory710.7 KiB
Average record size in memory112.0 B

Variable types

NUM13
BOOL1

Warnings

citric_acid has 151 (2.3%) zeros Zeros

Reproduction

Analysis started2021-02-27 19:39:44.947041
Analysis finished2021-02-27 19:40:42.531906
Duration57.58 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct4898
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2042.535632
Minimum0
Maximum4897
Zeros2
Zeros (%)< 0.1%
Memory size50.9 KiB
2021-02-27T20:40:42.761343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162
Q1812
median1649
Q33273
95-th percentile4572.2
Maximum4897
Range4897
Interquartile range (IQR)2461

Descriptive statistics

Standard deviation1436.926393
Coefficient of variation (CV)0.7035012612
Kurtosis-1.11588531
Mean2042.535632
Median Absolute Deviation (MAD)1100
Skewness0.4104190593
Sum13270354
Variance2064757.459
MonotocityNot monotonic
2021-02-27T20:40:43.138797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02< 0.1%
 
11422< 0.1%
 
10902< 0.1%
 
10942< 0.1%
 
10982< 0.1%
 
11022< 0.1%
 
11062< 0.1%
 
11102< 0.1%
 
11142< 0.1%
 
11182< 0.1%
 
11222< 0.1%
 
11262< 0.1%
 
11302< 0.1%
 
11342< 0.1%
 
11382< 0.1%
 
11462< 0.1%
 
10822< 0.1%
 
11502< 0.1%
 
11542< 0.1%
 
11582< 0.1%
 
11622< 0.1%
 
11662< 0.1%
 
11702< 0.1%
 
11742< 0.1%
 
11782< 0.1%
 
Other values (4873)644799.2%
 
ValueCountFrequency (%) 
02< 0.1%
 
12< 0.1%
 
22< 0.1%
 
32< 0.1%
 
42< 0.1%
 
52< 0.1%
 
62< 0.1%
 
72< 0.1%
 
82< 0.1%
 
92< 0.1%
 
ValueCountFrequency (%) 
48971< 0.1%
 
48961< 0.1%
 
48951< 0.1%
 
48941< 0.1%
 
48931< 0.1%
 
48921< 0.1%
 
48911< 0.1%
 
48901< 0.1%
 
48891< 0.1%
 
48881< 0.1%
 

fixed_acidity
Real number (ℝ≥0)

Distinct106
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.215307065
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:43.800503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.296433758
Coefficient of variation (CV)0.1796782516
Kurtosis5.061160665
Mean7.215307065
Median Absolute Deviation (MAD)0.6
Skewness1.723289647
Sum46877.85
Variance1.680740488
MonotocityNot monotonic
2021-02-27T20:40:44.415555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.83545.4%
 
6.63275.0%
 
6.43054.7%
 
72824.3%
 
6.92794.3%
 
7.22734.2%
 
6.72644.1%
 
7.12574.0%
 
6.52423.7%
 
7.42383.7%
 
7.32223.4%
 
6.22123.3%
 
6.32023.1%
 
7.61993.1%
 
61973.0%
 
7.51752.7%
 
6.11712.6%
 
7.81462.2%
 
7.71422.2%
 
5.81251.9%
 
81221.9%
 
7.91161.8%
 
5.91121.7%
 
8.21011.6%
 
8.3921.4%
 
Other values (81)134220.7%
 
ValueCountFrequency (%) 
3.81< 0.1%
 
3.91< 0.1%
 
4.22< 0.1%
 
4.43< 0.1%
 
4.51< 0.1%
 
4.62< 0.1%
 
4.760.1%
 
4.890.1%
 
4.980.1%
 
5300.5%
 
ValueCountFrequency (%) 
15.91< 0.1%
 
15.62< 0.1%
 
15.52< 0.1%
 
152< 0.1%
 
14.31< 0.1%
 
14.21< 0.1%
 
141< 0.1%
 
13.81< 0.1%
 
13.72< 0.1%
 
13.51< 0.1%
 

volatile_acidity
Real number (ℝ≥0)

Distinct187
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3396659997
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:44.794183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1646364741
Coefficient of variation (CV)0.4847010717
Kurtosis2.825372417
Mean0.3396659997
Median Absolute Deviation (MAD)0.08
Skewness1.495096542
Sum2206.81
Variance0.0271051686
MonotocityNot monotonic
2021-02-27T20:40:45.421071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.282864.4%
 
0.242664.1%
 
0.262563.9%
 
0.252383.7%
 
0.222353.6%
 
0.272323.6%
 
0.232213.4%
 
0.22173.3%
 
0.32143.3%
 
0.322053.2%
 
0.211973.0%
 
0.181872.9%
 
0.311782.7%
 
0.291762.7%
 
0.191722.6%
 
0.341652.5%
 
0.331542.4%
 
0.161432.2%
 
0.361422.2%
 
0.171402.2%
 
0.351081.7%
 
0.38981.5%
 
0.4961.5%
 
0.39961.5%
 
0.37891.4%
 
Other values (162)198630.6%
 
ValueCountFrequency (%) 
0.0840.1%
 
0.0851< 0.1%
 
0.091< 0.1%
 
0.160.1%
 
0.10560.1%
 
0.11130.2%
 
0.1153< 0.1%
 
0.12370.6%
 
0.1253< 0.1%
 
0.13440.7%
 
ValueCountFrequency (%) 
1.581< 0.1%
 
1.332< 0.1%
 
1.241< 0.1%
 
1.1851< 0.1%
 
1.181< 0.1%
 
1.131< 0.1%
 
1.1151< 0.1%
 
1.11< 0.1%
 
1.091< 0.1%
 
1.071< 0.1%
 

citric_acid
Real number (ℝ≥0)

ZEROS

Distinct89
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3186332153
Minimum0
Maximum1.66
Zeros151
Zeros (%)2.3%
Memory size50.9 KiB
2021-02-27T20:40:45.760173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1453178649
Coefficient of variation (CV)0.4560662791
Kurtosis2.397239216
Mean0.3186332153
Median Absolute Deviation (MAD)0.07
Skewness0.4717306725
Sum2070.16
Variance0.02111728186
MonotocityNot monotonic
2021-02-27T20:40:46.097450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.33375.2%
 
0.283014.6%
 
0.322894.4%
 
0.492834.4%
 
0.262574.0%
 
0.342493.8%
 
0.292443.8%
 
0.272363.6%
 
0.242323.6%
 
0.312303.5%
 
0.332083.2%
 
0.361973.0%
 
0.251632.5%
 
0.371532.4%
 
01512.3%
 
0.351502.3%
 
0.41462.2%
 
0.381362.1%
 
0.221312.0%
 
0.391292.0%
 
0.421241.9%
 
0.231081.7%
 
0.21991.5%
 
0.41981.5%
 
0.2951.5%
 
Other values (64)175127.0%
 
ValueCountFrequency (%) 
01512.3%
 
0.01400.6%
 
0.02560.9%
 
0.03320.5%
 
0.04410.6%
 
0.05250.4%
 
0.06300.5%
 
0.07340.5%
 
0.08370.6%
 
0.09420.6%
 
ValueCountFrequency (%) 
1.661< 0.1%
 
1.231< 0.1%
 
160.1%
 
0.991< 0.1%
 
0.912< 0.1%
 
0.881< 0.1%
 
0.861< 0.1%
 
0.822< 0.1%
 
0.812< 0.1%
 
0.82< 0.1%
 

residual_sugar
Real number (ℝ≥0)

Distinct316
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.443235339
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:46.441896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.757803743
Coefficient of variation (CV)0.8740764355
Kurtosis4.359271948
Mean5.443235339
Median Absolute Deviation (MAD)1.7
Skewness1.435404263
Sum35364.7
Variance22.63669646
MonotocityNot monotonic
2021-02-27T20:40:46.754605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22353.6%
 
1.82283.5%
 
1.62233.4%
 
1.42193.4%
 
1.21953.0%
 
2.21872.9%
 
2.11792.8%
 
1.91762.7%
 
1.71752.7%
 
1.51722.6%
 
1.31522.3%
 
2.31512.3%
 
1.11462.2%
 
2.41272.0%
 
2.51241.9%
 
2.61121.7%
 
1931.4%
 
2.8851.3%
 
2.7771.2%
 
2.9490.8%
 
4.6460.7%
 
5440.7%
 
3.2430.7%
 
7.8430.7%
 
3420.6%
 
Other values (291)317448.9%
 
ValueCountFrequency (%) 
0.62< 0.1%
 
0.770.1%
 
0.8250.4%
 
0.9410.6%
 
0.9540.1%
 
1931.4%
 
1.051< 0.1%
 
1.11462.2%
 
1.153< 0.1%
 
1.21953.0%
 
ValueCountFrequency (%) 
65.81< 0.1%
 
31.62< 0.1%
 
26.052< 0.1%
 
23.51< 0.1%
 
22.61< 0.1%
 
222< 0.1%
 
20.82< 0.1%
 
20.72< 0.1%
 
20.41< 0.1%
 
20.31< 0.1%
 

chlorides
Real number (ℝ≥0)

Distinct214
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05603386178
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:47.103278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.03503360137
Coefficient of variation (CV)0.6252219686
Kurtosis50.89805146
Mean0.05603386178
Median Absolute Deviation (MAD)0.011
Skewness5.399827732
Sum364.052
Variance0.001227353225
MonotocityNot monotonic
2021-02-27T20:40:47.399955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0442063.2%
 
0.0362003.1%
 
0.0421872.9%
 
0.0461852.8%
 
0.041822.8%
 
0.051822.8%
 
0.0481822.8%
 
0.0471752.7%
 
0.0451742.7%
 
0.0381692.6%
 
0.0341692.6%
 
0.0391612.5%
 
0.0371602.5%
 
0.0411512.3%
 
0.0431422.2%
 
0.0491412.2%
 
0.0531352.1%
 
0.0351302.0%
 
0.0331191.8%
 
0.0511161.8%
 
0.0521141.8%
 
0.0541121.7%
 
0.0321091.7%
 
0.031081.7%
 
0.0311071.6%
 
Other values (189)268141.3%
 
ValueCountFrequency (%) 
0.0091< 0.1%
 
0.0123< 0.1%
 
0.0131< 0.1%
 
0.01440.1%
 
0.01540.1%
 
0.01650.1%
 
0.01750.1%
 
0.018100.2%
 
0.01990.1%
 
0.02160.2%
 
ValueCountFrequency (%) 
0.6111< 0.1%
 
0.611< 0.1%
 
0.4671< 0.1%
 
0.4641< 0.1%
 
0.4221< 0.1%
 
0.4153< 0.1%
 
0.4142< 0.1%
 
0.4131< 0.1%
 
0.4031< 0.1%
 
0.4011< 0.1%
 

free_sulfur_dioxide
Real number (ℝ≥0)

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.52531938
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:47.775162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.74939977
Coefficient of variation (CV)0.5814648342
Kurtosis7.906238067
Mean30.52531938
Median Absolute Deviation (MAD)12
Skewness1.220066074
Sum198323
Variance315.0411923
MonotocityNot monotonic
2021-02-27T20:40:48.112111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
291832.8%
 
61702.6%
 
261612.5%
 
151572.4%
 
311522.3%
 
241522.3%
 
171492.3%
 
341462.2%
 
351442.2%
 
231422.2%
 
361382.1%
 
281352.1%
 
251352.1%
 
101342.1%
 
211342.1%
 
201312.0%
 
321312.0%
 
51292.0%
 
271282.0%
 
181261.9%
 
121261.9%
 
221241.9%
 
191231.9%
 
331231.9%
 
161191.8%
 
Other values (110)300546.3%
 
ValueCountFrequency (%) 
13< 0.1%
 
22< 0.1%
 
3590.9%
 
4520.8%
 
51292.0%
 
5.51< 0.1%
 
61702.6%
 
7961.5%
 
8911.4%
 
9911.4%
 
ValueCountFrequency (%) 
2891< 0.1%
 
146.51< 0.1%
 
138.51< 0.1%
 
1311< 0.1%
 
1281< 0.1%
 
1241< 0.1%
 
122.51< 0.1%
 
118.51< 0.1%
 
1121< 0.1%
 
1101< 0.1%
 

total_sulfur_dioxide
Real number (ℝ≥0)

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.7445744
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:48.461357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.52185452
Coefficient of variation (CV)0.488332648
Kurtosis-0.3716636549
Mean115.7445744
Median Absolute Deviation (MAD)39
Skewness-0.001177478234
Sum751992.5
Variance3194.720039
MonotocityNot monotonic
2021-02-27T20:40:48.778700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
111721.1%
 
113651.0%
 
122570.9%
 
117570.9%
 
98560.9%
 
114560.9%
 
124560.9%
 
128560.9%
 
118550.8%
 
150540.8%
 
119540.8%
 
133530.8%
 
110530.8%
 
140530.8%
 
125510.8%
 
126510.8%
 
101510.8%
 
131500.8%
 
149490.8%
 
104490.8%
 
134490.8%
 
116480.7%
 
156470.7%
 
142470.7%
 
28470.7%
 
Other values (251)516179.4%
 
ValueCountFrequency (%) 
63< 0.1%
 
740.1%
 
8140.2%
 
9150.2%
 
10280.4%
 
11260.4%
 
12290.4%
 
13280.4%
 
14330.5%
 
15350.5%
 
ValueCountFrequency (%) 
4401< 0.1%
 
366.51< 0.1%
 
3441< 0.1%
 
3131< 0.1%
 
307.51< 0.1%
 
3031< 0.1%
 
2941< 0.1%
 
2891< 0.1%
 
2821< 0.1%
 
2781< 0.1%
 

density
Real number (ℝ≥0)

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9946966338
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:49.143137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673004
Coefficient of variation (CV)0.003014660854
Kurtosis6.606066991
Mean0.9946966338
Median Absolute Deviation (MAD)0.00231
Skewness0.5036017301
Sum6462.54403
Variance8.992039783e-06
MonotocityNot monotonic
2021-02-27T20:40:49.501739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9972691.1%
 
0.9976691.1%
 
0.998641.0%
 
0.992641.0%
 
0.9928631.0%
 
0.9986610.9%
 
0.9962590.9%
 
0.9966590.9%
 
0.9968550.8%
 
0.9956550.8%
 
0.9934540.8%
 
0.9958540.8%
 
0.9932540.8%
 
0.9948540.8%
 
0.993520.8%
 
0.9982510.8%
 
0.9974500.8%
 
0.9938500.8%
 
0.9978490.8%
 
0.9944490.8%
 
0.9984490.8%
 
0.9927480.7%
 
0.9954480.7%
 
0.9924470.7%
 
0.9952460.7%
 
Other values (973)512478.9%
 
ValueCountFrequency (%) 
0.987111< 0.1%
 
0.987131< 0.1%
 
0.987221< 0.1%
 
0.98741< 0.1%
 
0.987422< 0.1%
 
0.987462< 0.1%
 
0.987581< 0.1%
 
0.987741< 0.1%
 
0.987791< 0.1%
 
0.987942< 0.1%
 
ValueCountFrequency (%) 
1.038981< 0.1%
 
1.01032< 0.1%
 
1.003692< 0.1%
 
1.00321< 0.1%
 
1.003153< 0.1%
 
1.002952< 0.1%
 
1.002891< 0.1%
 
1.00262< 0.1%
 
1.002422< 0.1%
 
1.002411< 0.1%
 

pH
Real number (ℝ≥0)

Distinct108
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.218500847
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:50.042063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607872021
Coefficient of variation (CV)0.04995717254
Kurtosis0.3676572674
Mean3.218500847
Median Absolute Deviation (MAD)0.11
Skewness0.3868387981
Sum20910.6
Variance0.02585252436
MonotocityNot monotonic
2021-02-27T20:40:50.434352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.162003.1%
 
3.141933.0%
 
3.221852.8%
 
3.21762.7%
 
3.151702.6%
 
3.191702.6%
 
3.181682.6%
 
3.241612.5%
 
3.121542.4%
 
3.11542.4%
 
3.171512.3%
 
3.31502.3%
 
3.261492.3%
 
3.231482.3%
 
3.081472.3%
 
3.251402.2%
 
3.361392.1%
 
3.111352.1%
 
3.211312.0%
 
3.321312.0%
 
3.131302.0%
 
3.281292.0%
 
3.291282.0%
 
3.061251.9%
 
3.271231.9%
 
Other values (83)271041.7%
 
ValueCountFrequency (%) 
2.721< 0.1%
 
2.742< 0.1%
 
2.771< 0.1%
 
2.793< 0.1%
 
2.83< 0.1%
 
2.821< 0.1%
 
2.8340.1%
 
2.841< 0.1%
 
2.8590.1%
 
2.86100.2%
 
ValueCountFrequency (%) 
4.012< 0.1%
 
3.92< 0.1%
 
3.851< 0.1%
 
3.821< 0.1%
 
3.811< 0.1%
 
3.82< 0.1%
 
3.791< 0.1%
 
3.782< 0.1%
 
3.772< 0.1%
 
3.762< 0.1%
 

sulphates
Real number (ℝ≥0)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5312682777
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:50.971056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1488058736
Coefficient of variation (CV)0.2800955372
Kurtosis8.653698823
Mean0.5312682777
Median Absolute Deviation (MAD)0.08
Skewness1.797270004
Sum3451.65
Variance0.02214318802
MonotocityNot monotonic
2021-02-27T20:40:51.399117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.52764.2%
 
0.462433.7%
 
0.542353.6%
 
0.442323.6%
 
0.382143.3%
 
0.482083.2%
 
0.522033.1%
 
0.491973.0%
 
0.471912.9%
 
0.451902.9%
 
0.531862.9%
 
0.421862.9%
 
0.41722.6%
 
0.431692.6%
 
0.561682.6%
 
0.581672.6%
 
0.511662.6%
 
0.61572.4%
 
0.391572.4%
 
0.551522.3%
 
0.591482.3%
 
0.411392.1%
 
0.571382.1%
 
0.371312.0%
 
0.621292.0%
 
Other values (86)194329.9%
 
ValueCountFrequency (%) 
0.221< 0.1%
 
0.231< 0.1%
 
0.2540.1%
 
0.2640.1%
 
0.27130.2%
 
0.28130.2%
 
0.29160.2%
 
0.3310.5%
 
0.31350.5%
 
0.32540.8%
 
ValueCountFrequency (%) 
21< 0.1%
 
1.981< 0.1%
 
1.952< 0.1%
 
1.621< 0.1%
 
1.611< 0.1%
 
1.591< 0.1%
 
1.561< 0.1%
 
1.363< 0.1%
 
1.341< 0.1%
 
1.331< 0.1%
 

alcohol
Real number (ℝ≥0)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.49180083
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:51.841268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.192711749
Coefficient of variation (CV)0.1136803651
Kurtosis-0.5316873829
Mean10.49180083
Median Absolute Deviation (MAD)0.9
Skewness0.5657177291
Sum68165.23
Variance1.422561316
MonotocityNot monotonic
2021-02-27T20:40:52.193311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.53675.6%
 
9.43325.1%
 
9.22714.2%
 
102293.5%
 
10.52273.5%
 
112173.3%
 
92153.3%
 
9.82143.3%
 
10.41943.0%
 
9.31933.0%
 
9.61872.9%
 
10.81772.7%
 
10.21762.7%
 
9.11672.6%
 
10.11612.5%
 
9.71592.4%
 
9.91582.4%
 
11.41532.4%
 
11.21482.3%
 
10.61422.2%
 
10.91372.1%
 
11.31332.0%
 
121231.9%
 
10.71231.9%
 
11.51181.8%
 
Other values (86)177627.3%
 
ValueCountFrequency (%) 
82< 0.1%
 
8.450.1%
 
8.5100.2%
 
8.6230.4%
 
8.7801.2%
 
8.81091.7%
 
8.9951.5%
 
92153.3%
 
9.051< 0.1%
 
9.11672.6%
 
ValueCountFrequency (%) 
14.91< 0.1%
 
14.21< 0.1%
 
14.051< 0.1%
 
14120.2%
 
13.93< 0.1%
 
13.82< 0.1%
 
13.770.1%
 
13.6130.2%
 
13.566666671< 0.1%
 
13.551< 0.1%
 

quality
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.818377713
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Memory size50.9 KiB
2021-02-27T20:40:52.569177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8732552715
Coefficient of variation (CV)0.1500856965
Kurtosis0.2323222693
Mean5.818377713
Median Absolute Deviation (MAD)1
Skewness0.1896226934
Sum37802
Variance0.7625747693
MonotocityNot monotonic
2021-02-27T20:40:52.910446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6283643.7%
 
5213832.9%
 
7107916.6%
 
42163.3%
 
81933.0%
 
3300.5%
 
950.1%
 
ValueCountFrequency (%) 
3300.5%
 
42163.3%
 
5213832.9%
 
6283643.7%
 
7107916.6%
 
81933.0%
 
950.1%
 
ValueCountFrequency (%) 
950.1%
 
81933.0%
 
7107916.6%
 
6283643.7%
 
5213832.9%
 
42163.3%
 
3300.5%
 

wine_type
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
0
4898 
1
1599 
ValueCountFrequency (%) 
0489875.4%
 
1159924.6%
 
2021-02-27T20:40:53.169815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-02-27T20:39:46.441554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:46.774272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:47.174103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:47.611912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:47.976027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:48.229878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:48.518380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:48.795235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:49.097306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:49.405298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:49.709573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:50.054881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:50.322533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:50.598489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:50.987068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:51.443838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:51.802629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:52.216394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:52.620054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:52.948489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:53.393675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:53.774014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:54.128014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:54.446410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:54.844053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:55.240475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:55.501839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:55.859432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:56.201214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:56.480279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:56.784210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:57.166702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:57.474002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:57.767685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:58.124186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:58.544023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:58.927499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:59.260079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:59.585711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:39:59.901583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:00.215771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:00.521647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:00.857580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:01.155710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:01.505589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:01.784748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:02.152700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:02.546986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:02.857603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:03.140937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:03.508140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:03.816586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:04.070521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:04.372591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:04.663526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:05.019678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:05.418033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:05.746365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:06.183559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:06.510200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:06.802628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:07.087765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:07.417657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:07.728731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:08.209428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:08.596346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:08.937786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:09.259372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:09.629718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:09.987957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:10.264515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:10.534178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:10.776256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:11.037766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:11.314374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:11.644955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:11.874461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:13.185848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:13.461210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:13.759742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:14.075936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:14.392844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:14.665252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:14.944923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:15.265393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:15.544406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:15.855173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:16.109923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:16.398829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:16.692198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:17.265607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:17.547337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:17.913628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:18.297687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:18.579845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:18.900639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:19.163875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:19.417381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:19.673363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:19.965571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:20.233237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:20.487438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:20.767600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:21.096333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:21.406838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:21.805421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:22.157403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:22.425446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:22.793819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:23.209648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:23.511750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:23.890052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:24.289525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:24.541280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:24.827820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:25.149816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:25.479538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:25.755481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:26.059842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:26.386256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:26.726898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:27.085962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:27.380450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:27.686430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:28.093896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:28.371528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:28.861162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:29.144501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:29.462614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:29.701486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:29.886212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:30.107112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:30.295683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:30.531066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:30.765191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:30.974747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:31.167313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:31.407729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:31.639886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:31.826514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:32.039462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:32.354969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:32.646647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:32.909897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:33.198386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:33.552134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:33.830779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:34.138471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:34.474024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:34.740049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:35.030889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:35.298734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:35.585292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:35.874580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:36.181970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:36.458264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:36.750845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:37.090196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:37.398484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:37.776005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:38.169362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:38.452647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:38.754848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:39.115991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:39.395389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:39.779705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:40.152593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:40.521621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:40.865319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-02-27T20:40:53.473744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-27T20:40:54.109498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-27T20:40:54.470208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-27T20:40:54.779300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-27T20:40:41.603239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-27T20:40:42.208670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualitywine_type
007.4000.7000.0001.9000.07611.00034.0000.9983.5100.5609.40051
117.8000.8800.0002.6000.09825.00067.0000.9973.2000.6809.80051
227.8000.7600.0402.3000.09215.00054.0000.9973.2600.6509.80051
3311.2000.2800.5601.9000.07517.00060.0000.9983.1600.5809.80061
447.4000.7000.0001.9000.07611.00034.0000.9983.5100.5609.40051
557.4000.6600.0001.8000.07513.00040.0000.9983.5100.5609.40051
667.9000.6000.0601.6000.06915.00059.0000.9963.3000.4609.40051
777.3000.6500.0001.2000.06515.00021.0000.9953.3900.47010.00071
887.8000.5800.0202.0000.0739.00018.0000.9973.3600.5709.50071
997.5000.5000.3606.1000.07117.000102.0000.9983.3500.80010.50051

Last rows

df_indexfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualitywine_type
648748886.8000.2200.3601.2000.05238.000127.0000.9933.0400.5409.20050
648848894.9000.2350.27011.7500.03034.000118.0000.9953.0700.5009.40060
648948906.1000.3400.2902.2000.03625.000100.0000.9893.0600.44011.80060
649048915.7000.2100.3200.9000.03838.000121.0000.9913.2400.46010.60060
649148926.5000.2300.3801.3000.03229.000112.0000.9933.2900.5409.70050
649248936.2000.2100.2901.6000.03924.00092.0000.9913.2700.50011.20060
649348946.6000.3200.3608.0000.04757.000168.0000.9953.1500.4609.60050
649448956.5000.2400.1901.2000.04130.000111.0000.9932.9900.4609.40060
649548965.5000.2900.3001.1000.02220.000110.0000.9893.3400.38012.80070
649648976.0000.2100.3800.8000.02022.00098.0000.9893.2600.32011.80060